Overview

Dataset statistics

Number of variables15
Number of observations8390
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory983.3 KiB
Average record size in memory120.0 B

Variable types

Categorical1
Numeric14

Alerts

Symbol has a high cardinality: 2295 distinct values High cardinality
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
D(Overall,S) is highly correlated with D(Overall,G)High correlation
D(Overall,G) is highly correlated with D(Overall,S)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 1 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
D(Overall,S) is highly correlated with D(Overall,G)High correlation
D(Overall,G) is highly correlated with D(Overall,S)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
mean-return is highly correlated with VaR (95%)High correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
kurtosis is highly correlated with skewHigh correlation
skew is highly correlated with kurtosisHigh correlation
VaR (95%) is highly correlated with mean-return and 1 other fieldsHigh correlation
semi-variance (down) is highly skewed (γ1 = 42.97666496) Skewed
D(Overall,S) is highly skewed (γ1 = 24.33626538) Skewed
ESG Score has unique values Unique
semi-variance (down) has unique values Unique
kurtosis has unique values Unique
skew has unique values Unique
D(Overall,E) has 2096 (25.0%) zeros Zeros
D(Overall,S) has 1325 (15.8%) zeros Zeros
D(Overall,G) has 1324 (15.8%) zeros Zeros
D(ESG, VaR) has 1648 (19.6%) zeros Zeros

Reproduction

Analysis started2022-09-24 08:46:27.401649
Analysis finished2022-09-24 08:46:46.159789
Duration18.76 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Symbol
Categorical

HIGH CARDINALITY

Distinct2295
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size65.7 KiB
GIII.OQ
 
7
BBY.N
 
7
AEO.N
 
7
HIBB.OQ
 
7
VSTO.N
 
7
Other values (2290)
8355 

Length

Max length8
Median length7
Mean length5.828843862
Min length3

Characters and Unicode

Total characters48904
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403 ?
Unique (%)4.8%

Sample

1st row360.AX
2nd row360.AX
3rd rowA.N
4th rowA.N
5th rowA.N

Common Values

ValueCountFrequency (%)
GIII.OQ7
 
0.1%
BBY.N7
 
0.1%
AEO.N7
 
0.1%
HIBB.OQ7
 
0.1%
VSTO.N7
 
0.1%
KMX.N7
 
0.1%
WSM.N7
 
0.1%
EXPR.N7
 
0.1%
ENS.N7
 
0.1%
LOW.N7
 
0.1%
Other values (2285)8320
99.2%

Length

2022-09-24T09:46:46.205351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
giii.oq7
 
0.1%
avav.oq7
 
0.1%
bby.n7
 
0.1%
mod.n7
 
0.1%
dds.n7
 
0.1%
dg.n7
 
0.1%
gme.n7
 
0.1%
dbi.n7
 
0.1%
casy.oq7
 
0.1%
schl.oq7
 
0.1%
Other values (2285)8320
99.2%

Most occurring characters

ValueCountFrequency (%)
.8390
17.2%
N6321
12.9%
O4575
 
9.4%
Q3554
 
7.3%
C2208
 
4.5%
A2080
 
4.3%
S1906
 
3.9%
T1817
 
3.7%
R1760
 
3.6%
M1456
 
3.0%
Other values (25)14837
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter40466
82.7%
Other Punctuation8390
 
17.2%
Lowercase Letter38
 
0.1%
Decimal Number6
 
< 0.1%
Connector Punctuation4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N6321
15.6%
O4575
 
11.3%
Q3554
 
8.8%
C2208
 
5.5%
A2080
 
5.1%
S1906
 
4.7%
T1817
 
4.5%
R1760
 
4.3%
M1456
 
3.6%
I1445
 
3.6%
Other values (16)13344
33.0%
Lowercase Letter
ValueCountFrequency (%)
a23
60.5%
b10
26.3%
p4
 
10.5%
q1
 
2.6%
Decimal Number
ValueCountFrequency (%)
32
33.3%
62
33.3%
02
33.3%
Other Punctuation
ValueCountFrequency (%)
.8390
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin40504
82.8%
Common8400
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N6321
15.6%
O4575
 
11.3%
Q3554
 
8.8%
C2208
 
5.5%
A2080
 
5.1%
S1906
 
4.7%
T1817
 
4.5%
R1760
 
4.3%
M1456
 
3.6%
I1445
 
3.6%
Other values (20)13382
33.0%
Common
ValueCountFrequency (%)
.8390
99.9%
_4
 
< 0.1%
32
 
< 0.1%
62
 
< 0.1%
02
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII48904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.8390
17.2%
N6321
12.9%
O4575
 
9.4%
Q3554
 
7.3%
C2208
 
4.5%
A2080
 
4.3%
S1906
 
3.9%
T1817
 
3.7%
R1760
 
3.6%
M1456
 
3.0%
Other values (25)14837
30.3%

Year
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.890942
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:46.267365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12018
median2019
Q32020
95-th percentile2021
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.672251556
Coefficient of variation (CV)0.0008283020749
Kurtosis-1.034924958
Mean2018.890942
Median Absolute Deviation (MAD)1
Skewness-0.2705443575
Sum16938495
Variance2.796425267
MonotonicityNot monotonic
2022-09-24T09:46:46.323473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20201858
22.1%
20191570
18.7%
20211558
18.6%
20181244
14.8%
20171043
12.4%
2016985
11.7%
2022132
 
1.6%
ValueCountFrequency (%)
2016985
11.7%
20171043
12.4%
20181244
14.8%
20191570
18.7%
20201858
22.1%
20211558
18.6%
2022132
 
1.6%
ValueCountFrequency (%)
2022132
 
1.6%
20211558
18.6%
20201858
22.1%
20191570
18.7%
20181244
14.8%
20171043
12.4%
2016985
11.7%

ESG Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.1775578
Minimum3.806763196
Maximum94.44445553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:46.404495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.806763196
5-th percentile17.02965548
Q130.5849707
median43.18626799
Q359.03944378
95-th percentile78.481338
Maximum94.44445553
Range90.63769233
Interquartile range (IQR)28.45447307

Descriptive statistics

Standard deviation18.82265547
Coefficient of variation (CV)0.4166372947
Kurtosis-0.7115598409
Mean45.1775578
Median Absolute Deviation (MAD)14.05672394
Skewness0.2793725942
Sum379039.71
Variance354.2923588
MonotonicityNot monotonic
2022-09-24T09:46:46.487513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.081018031
 
< 0.1%
26.878664451
 
< 0.1%
15.585325931
 
< 0.1%
17.05280311
 
< 0.1%
43.183687071
 
< 0.1%
27.403196281
 
< 0.1%
28.907810271
 
< 0.1%
27.521227721
 
< 0.1%
28.881561021
 
< 0.1%
67.45375511
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
3.8067631961
< 0.1%
5.1148336641
< 0.1%
5.7764849911
< 0.1%
5.7944441271
< 0.1%
5.9749974951
< 0.1%
6.1522211931
< 0.1%
6.2216585671
< 0.1%
6.6863983551
< 0.1%
6.814182151
< 0.1%
6.8681014031
< 0.1%
ValueCountFrequency (%)
94.444455531
< 0.1%
93.539125651
< 0.1%
93.327841241
< 0.1%
92.806508361
< 0.1%
92.619923141
< 0.1%
92.315162631
< 0.1%
92.14299521
< 0.1%
91.905084061
< 0.1%
91.855033491
< 0.1%
91.476861231
< 0.1%

Environmental Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7249
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.66414699
Minimum0.027777778
Maximum97.98022505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:46.573533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.027777778
5-th percentile1.830951138
Q19.301939765
median25.35933463
Q352.52109455
95-th percentile81.62031074
Maximum97.98022505
Range97.95244727
Interquartile range (IQR)43.21915478

Descriptive statistics

Standard deviation26.09604144
Coefficient of variation (CV)0.7989200347
Kurtosis-0.8080771856
Mean32.66414699
Median Absolute Deviation (MAD)18.97278
Skewness0.6148309379
Sum274052.1932
Variance681.003379
MonotonicityNot monotonic
2022-09-24T09:46:46.661553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.15270351121
 
1.4%
1.74058178452
 
0.6%
1.77177177239
 
0.5%
1.60882140333
 
0.4%
17.5356921220
 
0.2%
20.3940886720
 
0.2%
37.3170731718
 
0.2%
4.94505494518
 
0.2%
1.92481884116
 
0.2%
6.30252100816
 
0.2%
Other values (7239)8037
95.8%
ValueCountFrequency (%)
0.0277777781
< 0.1%
0.0869565221
< 0.1%
0.0885935771
< 0.1%
0.0916722961
< 0.1%
0.1421464111
< 0.1%
0.1424501421
< 0.1%
0.1462971261
< 0.1%
0.1944444441
< 0.1%
0.2170138891
< 0.1%
0.2555583951
< 0.1%
ValueCountFrequency (%)
97.980225051
< 0.1%
97.697050951
< 0.1%
97.255203031
< 0.1%
97.168150591
< 0.1%
97.160908831
< 0.1%
97.022995931
< 0.1%
96.826191381
< 0.1%
96.679221371
< 0.1%
96.622060021
< 0.1%
96.542736541
< 0.1%

Social Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8352
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.89734695
Minimum0.453490413
Maximum97.83313378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:46.751574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.453490413
5-th percentile15.27588208
Q130.20751794
median45.20856376
Q362.31384454
95-th percentile84.58797876
Maximum97.83313378
Range97.37964337
Interquartile range (IQR)32.1063266

Descriptive statistics

Standard deviation21.13387719
Coefficient of variation (CV)0.4506412104
Kurtosis-0.7259666039
Mean46.89734695
Median Absolute Deviation (MAD)15.90164398
Skewness0.2685154
Sum393468.7409
Variance446.6407652
MonotonicityNot monotonic
2022-09-24T09:46:46.838593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.953331054
 
< 0.1%
25.10542163
 
< 0.1%
503
 
< 0.1%
25.916863393
 
< 0.1%
28.965558253
 
< 0.1%
22.472511383
 
< 0.1%
31.068697953
 
< 0.1%
23.843056713
 
< 0.1%
35.395305782
 
< 0.1%
10.93752
 
< 0.1%
Other values (8342)8361
99.7%
ValueCountFrequency (%)
0.4534904131
< 0.1%
1.2509850281
< 0.1%
1.3741856681
< 0.1%
2.0584982181
< 0.1%
2.2603485841
< 0.1%
2.4409562211
< 0.1%
2.7040716291
< 0.1%
2.7087430721
< 0.1%
2.8623239941
< 0.1%
2.8683574881
< 0.1%
ValueCountFrequency (%)
97.833133781
< 0.1%
97.693704961
< 0.1%
97.668742951
< 0.1%
97.655062681
< 0.1%
97.579012441
< 0.1%
97.394131821
< 0.1%
97.392935251
< 0.1%
97.348331381
< 0.1%
97.256723051
< 0.1%
97.234743751
< 0.1%

Governance Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8359
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.00145002
Minimum0.713528414
Maximum99.49668624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:46.927613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.713528414
5-th percentile16.13415441
Q136.54946887
median54.65678837
Q370.23126631
95-th percentile85.36482267
Maximum99.49668624
Range98.78315783
Interquartile range (IQR)33.68179744

Descriptive statistics

Standard deviation21.54495779
Coefficient of variation (CV)0.4064975162
Kurtosis-0.8158104855
Mean53.00145002
Median Absolute Deviation (MAD)16.75195087
Skewness-0.2135485784
Sum444682.1657
Variance464.1852061
MonotonicityNot monotonic
2022-09-24T09:46:47.012632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.430628532
 
< 0.1%
73.417235492
 
< 0.1%
25.723148152
 
< 0.1%
62.462575712
 
< 0.1%
35.748197452
 
< 0.1%
60.886359212
 
< 0.1%
70.701080772
 
< 0.1%
18.788167942
 
< 0.1%
51.315585142
 
< 0.1%
29.010814252
 
< 0.1%
Other values (8349)8370
99.8%
ValueCountFrequency (%)
0.7135284141
< 0.1%
1.218940231
< 0.1%
1.4498058791
< 0.1%
1.6215289681
< 0.1%
1.6709328781
< 0.1%
1.6936860071
< 0.1%
1.7419423241
< 0.1%
1.773748941
< 0.1%
1.9581911261
< 0.1%
2.0065415241
< 0.1%
ValueCountFrequency (%)
99.496686241
< 0.1%
98.610655441
< 0.1%
98.299313361
< 0.1%
98.14167411
< 0.1%
97.52325211
< 0.1%
97.499648791
< 0.1%
97.301438521
< 0.1%
97.028115551
< 0.1%
96.773164111
< 0.1%
96.673930581
< 0.1%

mean-return
Real number (ℝ)

HIGH CORRELATION

Distinct8385
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002510868964
Minimum-0.552090105
Maximum0.5359062215
Zeros0
Zeros (%)0.0%
Negative3485
Negative (%)41.5%
Memory size65.7 KiB
2022-09-24T09:46:47.099653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.552090105
5-th percentile-0.07413523606
Q1-0.01588608457
median0.00623143584
Q30.02532615983
95-th percentile0.06483100316
Maximum0.5359062215
Range1.087996327
Interquartile range (IQR)0.0412122444

Descriptive statistics

Standard deviation0.04782904166
Coefficient of variation (CV)19.04880038
Kurtosis13.67467632
Mean0.002510868964
Median Absolute Deviation (MAD)0.02046234336
Skewness-1.195183679
Sum21.0661906
Variance0.002287617226
MonotonicityNot monotonic
2022-09-24T09:46:47.185671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0394704742
 
< 0.1%
0.063013380052
 
< 0.1%
0.031460944612
 
< 0.1%
0.011522882332
 
< 0.1%
1.513940488 × 10-172
 
< 0.1%
-0.033986503021
 
< 0.1%
0.063247621591
 
< 0.1%
-0.0076424416341
 
< 0.1%
0.035432582881
 
< 0.1%
0.01750590221
 
< 0.1%
Other values (8375)8375
99.8%
ValueCountFrequency (%)
-0.5520901051
< 0.1%
-0.53041388231
< 0.1%
-0.49257455991
< 0.1%
-0.40940968471
< 0.1%
-0.40191581671
< 0.1%
-0.38965559381
< 0.1%
-0.34839798221
< 0.1%
-0.33464189151
< 0.1%
-0.33079360641
< 0.1%
-0.313157331
< 0.1%
ValueCountFrequency (%)
0.53590622151
< 0.1%
0.28352657881
< 0.1%
0.25580976621
< 0.1%
0.25518092261
< 0.1%
0.24944734791
< 0.1%
0.23766776791
< 0.1%
0.23212046331
< 0.1%
0.22828886541
< 0.1%
0.22062058531
< 0.1%
0.21998294721
< 0.1%

semi-variance (down)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03176567232
Minimum2.049697647 × 10-6
Maximum10.60379622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:47.273232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.049697647 × 10-6
5-th percentile0.001232310146
Q10.004012757425
median0.009379741113
Q30.02486660118
95-th percentile0.109064559
Maximum10.60379622
Range10.60379417
Interquartile range (IQR)0.02085384376

Descriptive statistics

Standard deviation0.1558296352
Coefficient of variation (CV)4.905598523
Kurtosis2639.649959
Mean0.03176567232
Median Absolute Deviation (MAD)0.006808096917
Skewness42.97666496
Sum266.5139907
Variance0.0242828752
MonotonicityNot monotonic
2022-09-24T09:46:47.361251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0040211727321
 
< 0.1%
0.028867408191
 
< 0.1%
0.14019616881
 
< 0.1%
0.0050149932731
 
< 0.1%
0.011637528831
 
< 0.1%
0.0064751258431
 
< 0.1%
0.071404742831
 
< 0.1%
0.031335356661
 
< 0.1%
0.026504264431
 
< 0.1%
0.017772675351
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
2.049697647 × 10-61
< 0.1%
5.055098442 × 10-61
< 0.1%
5.08445488 × 10-61
< 0.1%
5.274323069 × 10-61
< 0.1%
6.131105551 × 10-61
< 0.1%
8.070466148 × 10-61
< 0.1%
1.132803218 × 10-51
< 0.1%
1.166024936 × 10-51
< 0.1%
1.237343283 × 10-51
< 0.1%
1.319121727 × 10-51
< 0.1%
ValueCountFrequency (%)
10.603796221
< 0.1%
4.4012740891
< 0.1%
2.8147659031
< 0.1%
2.6603798661
< 0.1%
2.5282397241
< 0.1%
2.2642717071
< 0.1%
1.7008892181
< 0.1%
1.6839319681
< 0.1%
1.2747443511
< 0.1%
1.2739383671
< 0.1%

kurtosis
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.676361517
Minimum-4.771690305
Maximum10.66785002
Zeros0
Zeros (%)0.0%
Negative3710
Negative (%)44.2%
Memory size65.7 KiB
2022-09-24T09:46:47.455273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.771690305
5-th percentile-1.397095342
Q1-0.6374184458
median0.2110665923
Q31.478189565
95-th percentile4.462382098
Maximum10.66785002
Range15.43954032
Interquartile range (IQR)2.115608011

Descriptive statistics

Standard deviation1.848141859
Coefficient of variation (CV)2.732476364
Kurtosis2.280649397
Mean0.676361517
Median Absolute Deviation (MAD)0.9766243352
Skewness1.393491393
Sum5674.673128
Variance3.41562833
MonotonicityNot monotonic
2022-09-24T09:46:47.538291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.63282574091
 
< 0.1%
2.0940137831
 
< 0.1%
5.7915091421
 
< 0.1%
0.58111945141
 
< 0.1%
-1.3619041491
 
< 0.1%
-0.6689741971
 
< 0.1%
-0.82171287791
 
< 0.1%
0.37175294721
 
< 0.1%
0.16679804811
 
< 0.1%
0.27974915251
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
-4.7716903051
< 0.1%
-3.3034801381
< 0.1%
-3.2836624931
< 0.1%
-3.0102317641
< 0.1%
-2.7756234541
< 0.1%
-2.7722840671
< 0.1%
-2.7681182631
< 0.1%
-2.7533305481
< 0.1%
-2.7195872971
< 0.1%
-2.7124496211
< 0.1%
ValueCountFrequency (%)
10.667850021
< 0.1%
10.396198261
< 0.1%
10.392304821
< 0.1%
9.9911235261
< 0.1%
9.3306737851
< 0.1%
9.2727962921
< 0.1%
9.1362195551
< 0.1%
8.9692546151
< 0.1%
8.9098639571
< 0.1%
8.8672274541
< 0.1%

skew
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1355040776
Minimum-3.243905543
Maximum3.118139779
Zeros1
Zeros (%)< 0.1%
Negative4739
Negative (%)56.5%
Memory size65.7 KiB
2022-09-24T09:46:47.625357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.243905543
5-th percentile-1.63404865
Q1-0.7022596437
median-0.1395536166
Q30.4448158851
95-th percentile1.326255899
Maximum3.118139779
Range6.362045322
Interquartile range (IQR)1.147075529

Descriptive statistics

Standard deviation0.8902627537
Coefficient of variation (CV)-6.570007115
Kurtosis0.2027304549
Mean-0.1355040776
Median Absolute Deviation (MAD)0.5739383982
Skewness0.007182582218
Sum-1136.879211
Variance0.7925677707
MonotonicityNot monotonic
2022-09-24T09:46:47.706378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.061141406491
 
< 0.1%
1.2082380141
 
< 0.1%
-2.224821931
 
< 0.1%
0.23024021141
 
< 0.1%
0.37951954941
 
< 0.1%
0.6718853871
 
< 0.1%
-0.40621576821
 
< 0.1%
-0.67593515961
 
< 0.1%
0.99501466761
 
< 0.1%
-0.79310068971
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
-3.2439055431
< 0.1%
-3.1926246111
< 0.1%
-3.1921451011
< 0.1%
-2.957153091
< 0.1%
-2.915910351
< 0.1%
-2.84101981
< 0.1%
-2.8391570011
< 0.1%
-2.8164677981
< 0.1%
-2.8032260221
< 0.1%
-2.7941864191
< 0.1%
ValueCountFrequency (%)
3.1181397791
< 0.1%
2.9395501841
< 0.1%
2.9006242261
< 0.1%
2.8469504781
< 0.1%
2.8150819581
< 0.1%
2.7816161991
< 0.1%
2.7276243631
< 0.1%
2.6926845921
< 0.1%
2.6740595041
< 0.1%
2.5790034581
< 0.1%

VaR (95%)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8388
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.175101922
Minimum-5.756462732
Maximum0.05449763172
Zeros1
Zeros (%)< 0.1%
Negative8369
Negative (%)99.7%
Memory size65.7 KiB
2022-09-24T09:46:47.793398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5.756462732
5-th percentile-0.4635915275
Q1-0.2212239879
median-0.130744321
Q3-0.07474922092
95-th percentile-0.02993538288
Maximum0.05449763172
Range5.810960364
Interquartile range (IQR)0.146474767

Descriptive statistics

Standard deviation0.173004206
Coefficient of variation (CV)-0.9880200288
Kurtosis154.8121932
Mean-0.175101922
Median Absolute Deviation (MAD)0.06688171536
Skewness-7.096374391
Sum-1469.105126
Variance0.0299304553
MonotonicityNot monotonic
2022-09-24T09:46:47.878417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.26531412552
 
< 0.1%
-0.34657359032
 
< 0.1%
-0.032123995111
 
< 0.1%
-0.15256093111
 
< 0.1%
-0.12463840091
 
< 0.1%
-0.47048055451
 
< 0.1%
-0.19658915051
 
< 0.1%
-0.26121740251
 
< 0.1%
-0.15135214461
 
< 0.1%
-0.092001413691
 
< 0.1%
Other values (8378)8378
99.9%
ValueCountFrequency (%)
-5.7564627321
< 0.1%
-3.4538776391
< 0.1%
-1.9927252371
< 0.1%
-1.7856851141
< 0.1%
-1.6699980661
< 0.1%
-1.644567561
< 0.1%
-1.6279021631
< 0.1%
-1.6094379121
< 0.1%
-1.5652834211
< 0.1%
-1.5306459461
< 0.1%
ValueCountFrequency (%)
0.054497631721
< 0.1%
0.051156618591
< 0.1%
0.032876869491
< 0.1%
0.030358517291
< 0.1%
0.027034805881
< 0.1%
0.013517765121
< 0.1%
0.010063965991
< 0.1%
0.0092847923411
< 0.1%
0.0057931581391
< 0.1%
0.0049907767871
< 0.1%

D(Overall,E)
Real number (ℝ≥0)

ZEROS

Distinct6295
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1892791368
Minimum0
Maximum37.02186133
Zeros2096
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:47.970438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.981538573 × 10-9
median0.004167477255
Q30.03957941108
95-th percentile0.7938215578
Maximum37.02186133
Range37.02186133
Interquartile range (IQR)0.03957940709

Descriptive statistics

Standard deviation1.026972824
Coefficient of variation (CV)5.425705344
Kurtosis354.8552514
Mean0.1892791368
Median Absolute Deviation (MAD)0.004167477255
Skewness15.19435208
Sum1588.051958
Variance1.054673181
MonotonicityNot monotonic
2022-09-24T09:46:48.057458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02096
 
25.0%
0.01170589181
 
< 0.1%
0.0017962652651
 
< 0.1%
0.029069495251
 
< 0.1%
0.33300181771
 
< 0.1%
3.192027671
 
< 0.1%
0.030453209431
 
< 0.1%
0.0010919507131
 
< 0.1%
0.027842399751
 
< 0.1%
0.032887440511
 
< 0.1%
Other values (6285)6285
74.9%
ValueCountFrequency (%)
02096
25.0%
2.58784839 × 10-91
 
< 0.1%
3.217779728 × 10-91
 
< 0.1%
6.272815109 × 10-91
 
< 0.1%
8.881527078 × 10-91
 
< 0.1%
1.183434085 × 10-81
 
< 0.1%
1.228725574 × 10-81
 
< 0.1%
2.213180064 × 10-81
 
< 0.1%
4.093839728 × 10-81
 
< 0.1%
5.646151584 × 10-81
 
< 0.1%
ValueCountFrequency (%)
37.021861331
< 0.1%
26.531640611
< 0.1%
22.902571211
< 0.1%
19.885757981
< 0.1%
17.982224011
< 0.1%
16.850680891
< 0.1%
16.785940491
< 0.1%
15.53792421
< 0.1%
14.295571511
< 0.1%
13.47497851
< 0.1%

D(Overall,S)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7066
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03253304097
Minimum0
Maximum7.537939412
Zeros1325
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:48.516562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0001889476683
median0.003962793392
Q30.0215166759
95-th percentile0.1328632123
Maximum7.537939412
Range7.537939412
Interquartile range (IQR)0.02132772823

Descriptive statistics

Standard deviation0.1501725516
Coefficient of variation (CV)4.616001058
Kurtosis939.9890514
Mean0.03253304097
Median Absolute Deviation (MAD)0.003962793392
Skewness24.33626538
Sum272.9522138
Variance0.02255179524
MonotonicityNot monotonic
2022-09-24T09:46:48.599581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01325
 
15.8%
0.14949139521
 
< 0.1%
0.00014098775321
 
< 0.1%
0.001844099461
 
< 0.1%
0.010713309581
 
< 0.1%
0.0042980791431
 
< 0.1%
9.981394915 × 10-51
 
< 0.1%
0.0015585070411
 
< 0.1%
0.00019610210171
 
< 0.1%
0.0020894081021
 
< 0.1%
Other values (7056)7056
84.1%
ValueCountFrequency (%)
01325
15.8%
3.981603671 × 10-91
 
< 0.1%
4.58263128 × 10-91
 
< 0.1%
6.680347353 × 10-91
 
< 0.1%
1.076202855 × 10-81
 
< 0.1%
2.148256998 × 10-81
 
< 0.1%
2.240454361 × 10-81
 
< 0.1%
2.249729676 × 10-81
 
< 0.1%
2.277855601 × 10-81
 
< 0.1%
2.74393533 × 10-81
 
< 0.1%
ValueCountFrequency (%)
7.5379394121
< 0.1%
4.7451996911
< 0.1%
3.3871800881
< 0.1%
2.6152502311
< 0.1%
2.3080306141
< 0.1%
2.2487601261
< 0.1%
2.2205620681
< 0.1%
2.1872262621
< 0.1%
1.9354631851
< 0.1%
1.9340698761
< 0.1%

D(Overall,G)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7067
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05577985677
Minimum0
Maximum6.730564985
Zeros1324
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:48.688973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0003379015713
median0.006928694293
Q30.0410071533
95-th percentile0.2669075591
Maximum6.730564985
Range6.730564985
Interquartile range (IQR)0.04066925173

Descriptive statistics

Standard deviation0.1783318043
Coefficient of variation (CV)3.197064579
Kurtosis329.8195008
Mean0.05577985677
Median Absolute Deviation (MAD)0.006928694293
Skewness13.43502759
Sum467.9929983
Variance0.03180223242
MonotonicityNot monotonic
2022-09-24T09:46:48.772995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01324
 
15.8%
0.054911670771
 
< 0.1%
0.1967472221
 
< 0.1%
0.0020462430141
 
< 0.1%
0.096519950541
 
< 0.1%
0.022087112221
 
< 0.1%
0.0016631217661
 
< 0.1%
0.0013754231421
 
< 0.1%
0.00050501152831
 
< 0.1%
0.0047189020131
 
< 0.1%
Other values (7057)7057
84.1%
ValueCountFrequency (%)
01324
15.8%
7.816879023 × 10-121
 
< 0.1%
9.840756363 × 10-111
 
< 0.1%
2.457006467 × 10-101
 
< 0.1%
9.647269977 × 10-101
 
< 0.1%
2.252115497 × 10-81
 
< 0.1%
2.729156174 × 10-81
 
< 0.1%
3.387613642 × 10-81
 
< 0.1%
4.679192194 × 10-81
 
< 0.1%
4.766318522 × 10-81
 
< 0.1%
ValueCountFrequency (%)
6.7305649851
< 0.1%
3.485325511
< 0.1%
3.4737750461
< 0.1%
3.4057783011
< 0.1%
2.9348697661
< 0.1%
2.836289031
< 0.1%
2.7178942911
< 0.1%
2.7047767321
< 0.1%
2.4706002321
< 0.1%
2.376114811
< 0.1%

D(ESG, VaR)
Real number (ℝ≥0)

ZEROS

Distinct6743
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8386957782
Minimum0
Maximum47.28022229
Zeros1648
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-24T09:46:48.861015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.006944679042
median0.2164593412
Q30.8946971167
95-th percentile3.592166574
Maximum47.28022229
Range47.28022229
Interquartile range (IQR)0.8877524377

Descriptive statistics

Standard deviation1.916485438
Coefficient of variation (CV)2.285078199
Kurtosis136.7290299
Mean0.8386957782
Median Absolute Deviation (MAD)0.2164593412
Skewness8.628433334
Sum7036.657579
Variance3.672916435
MonotonicityNot monotonic
2022-09-24T09:46:48.945034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01648
 
19.6%
0.018971167941
 
< 0.1%
0.16181400611
 
< 0.1%
3.0623724111
 
< 0.1%
0.10388787281
 
< 0.1%
0.045165834941
 
< 0.1%
5.9408704281
 
< 0.1%
4.5623981041
 
< 0.1%
0.72369877081
 
< 0.1%
0.016921087551
 
< 0.1%
Other values (6733)6733
80.3%
ValueCountFrequency (%)
01648
19.6%
7.201370445 × 10-81
 
< 0.1%
1.615828905 × 10-71
 
< 0.1%
3.591858258 × 10-71
 
< 0.1%
5.596244496 × 10-71
 
< 0.1%
7.547698111 × 10-71
 
< 0.1%
1.053538485 × 10-61
 
< 0.1%
1.107233246 × 10-61
 
< 0.1%
1.207528765 × 10-61
 
< 0.1%
2.423971339 × 10-61
 
< 0.1%
ValueCountFrequency (%)
47.280222291
< 0.1%
47.011040471
< 0.1%
37.174199171
< 0.1%
35.044973961
< 0.1%
28.141407191
< 0.1%
25.853308391
< 0.1%
22.905634731
< 0.1%
22.430017651
< 0.1%
21.611530351
< 0.1%
20.238895161
< 0.1%

Interactions

2022-09-24T09:46:44.856620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:30.463103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.568988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.701880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.746117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.782352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.997626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.023859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.053091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.328380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.360614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.409851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.468090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.800745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.928635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:30.552123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.641004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.778898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.821134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.858368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.073643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.096875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.128108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.403397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.435631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.485869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.545108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.877762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.995651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:30.625139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.709019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.848914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.890150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.928385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.142659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.165890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.199127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.473413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.508647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.557884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.616124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.949779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.069668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:30.699156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.780035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.921931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.962166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.000401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.216676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.238907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.273141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.545429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.584664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.630901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.691141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.025795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.141684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:30.773172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.848051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.004949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.035182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.072417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.287692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.311925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.347158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.618446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.659681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.703917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.765158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.100812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.213701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:30.849825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.918067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.076966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.107198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.151435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.359708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.383940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.421176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.692462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.734698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.777934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.838174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.174830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.287717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:30.925842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.989083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.150982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.179214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.398490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.432724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.457957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.495191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.766479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.809715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.852951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.913191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.248847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.358733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.000860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.060099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.223998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.252232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.468507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.503741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.528973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.568208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.836495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.882733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.927968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.987208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.323275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.433750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.083877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.268148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.300016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.330249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.544524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.577757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.605990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.645225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.912512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.960749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.006986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.063225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.400292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.504767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.163897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.338799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.374033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.405267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.619541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.651774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.678006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.721242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.985528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.035766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.082003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.139242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.475309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.576783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.240913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.410815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.450050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.483284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.695558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.726791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.752023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.796259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.060545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.109783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.160021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.213612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.551326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.651650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.320932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.484832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.527067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.560301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.772575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.802809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.827040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:38.874277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.135562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.186800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.239038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.290629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.629344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.724667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.406951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.558848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.603085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.638319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.849593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.877826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.907058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.178346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.211580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.261817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.318057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.370647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.706586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:45.798687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:31.498973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:32.633865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:33.677101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:34.713336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:35.926610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:36.953843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:37.983076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:39.255363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:40.287597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:41.338835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:42.395074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:43.730729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-24T09:46:44.783603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-24T09:46:49.021051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-24T09:46:49.143078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-24T09:46:49.266107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-24T09:46:49.388134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-24T09:46:45.911709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-24T09:46:46.080620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(Overall,E)D(Overall,S)D(Overall,G)D(ESG, VaR)
0360.AX2020.027.0810188.91666743.01514418.8092710.0328460.004021-0.6328260.061141-0.0710750.0000000.0000000.0000000.000000
1360.AX2021.031.4109588.45360845.97221025.7593070.0258250.0058391.742724-0.646043-0.0752980.0406640.0066980.0275970.008210
2A.N2017.087.59506577.06065892.79237585.6679960.0355120.0070120.4557900.720648-0.0965250.0000820.0000690.0002393.607230
3A.N2018.089.48925378.04573794.20181788.624684-0.0610430.0044510.6103080.850044-0.1626270.0000760.0000400.0001570.250266
4A.N2019.088.33085579.33599794.50527384.398806-0.0292690.0160820.126243-0.828252-0.2284900.0008660.0002640.0012840.124653
5A.N2020.087.57748979.95897793.59937083.2039840.0456530.2709804.900414-1.988309-0.5202270.0002690.0000010.0000320.691122
6AA.N2016.087.18660585.83233081.59038698.2993130.0262760.0052540.036191-0.293729-0.0632960.0000000.0000000.0000000.000000
7AA.N2017.087.27310990.33374879.47214995.671099-0.0217860.0023200.566293-0.682118-0.0811010.0025120.0007450.0007890.060957
8AA.N2018.086.61997888.38353779.12899596.369097-0.0337960.006287-0.703649-0.791101-0.1379310.0002050.0000100.0002180.290054
9AA.N2019.088.07882587.34716383.47424796.673931-0.0396990.0151112.266191-1.274915-0.2232850.0008120.0013510.0001830.216216

Last rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(Overall,E)D(Overall,S)D(Overall,G)D(ESG, VaR)
8380ZUMZ.OQ2022.023.9722304.64646528.38228828.735048-0.1103700.004964-0.820491-0.116846-0.2001650.0026390.0030210.0028100.895958
8381ZUO.N2020.036.2875293.08333354.97065830.1605370.0172910.0185351.256732-0.901465-0.1824010.0000000.0533550.2241430.172335
8382ZUO.N2021.053.91878527.84870166.40737750.9857440.0628550.002580-0.5513960.410789-0.0168453.2571960.0428470.0166437.718136
8383ZUO.N2022.049.73006127.33643960.10000547.518473-0.0145640.004880-2.0065380.046289-0.1119540.0038820.0003580.0001093.900213
8384ZWS.N2016.022.3237633.08080831.54859133.3205350.0778670.040135-1.0327950.420287-0.1958290.0000000.0000000.0000000.000000
8385ZWS.N2017.021.7566034.25006027.08554135.777625-0.0290820.1070022.814027-1.311767-0.4914960.1207390.0160770.0093860.894813
8386ZWS.N2018.022.9664534.19339824.53159843.442041-0.1276200.074135-1.053860-0.274952-0.4616630.0045620.0234570.0195970.013628
8387ZWS.N2019.042.38505947.92359436.35408243.6460980.0129170.0354426.0092752.079474-0.2964573.3245510.0481440.3697551.114489
8388ZWS.N2020.059.56629853.79715376.73664343.977042-0.0384990.057422-0.321493-0.129591-0.3930050.0504820.1654690.1107160.003407
8389ZWS.N2021.059.80939170.17463676.56941925.396839-0.0174650.0105180.3711961.074856-0.1827110.0684840.0000390.3059370.592885